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- 저자 : Zeqi Hu (검색결과 2 건)
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Zeqi Hu,Xunpeng Qin,Yifeng Li,Mao Ni 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.4
In wire and arc additive manufacturing, the weld bead geometry determined the slicing layer height, which was decided by the welding parameters. Generally, the determination of the welding parameters relied on empirical and experimental data through the trial-and-error methods that incur considerable time and cost. To obtain the proper welding process parameters according to the desired single bead geometry and layer height, a full factorial experimental design matrix was applied to collect the original data of welding parameters and bead geometrical variables. A forward artificial neural network (FANN) was built to predict the bead geometry form the welding parameters. Then, a closed-loop iteration method combined a genetic algorithm (GA) and the FANN model (FANN-GA) was developed to search for the most optimal welding process parameters in accordance with the selected bead geometrical variables. The results confirmed that the FANN-GA model has a good performance on the backward prediction of the welding process parameters compared with the direct backward artificial neural network (BANN). Several groups of single layer multi-bead and multi-layer multi-bead experiment were performed to testify the proposed method, and the relative error between the desired and actual layer height was small. The proposed method makes it possible to fabricate the component with an arbitrary desired layer height, and could be used in the adaptive slicing additive manufacturing or surface coating.
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Xiaochen Xiong,Xunpeng Qin,Lin Hua,Gang Wan,Shilong Wei,Mao Ni,Zeqi Hu 대한금속·재료학회 2023 METALS AND MATERIALS International Vol.29 No.6
An in-situ follow-up hammering-assisted (FH) wire arc additive manufacturing (WAAM) process is proposed for hydraulicturbine blade repairing. With different hammering intervention temperatures above the austenite recrystallization temperature(Tre-γ), the influence and mechanism of the process on the grain size of prior austenite grains and room-temperaturemartensite, as well as the texture of 0Cr13Ni5Mo deposited layers are systematically studied. The OM, SEM and EBSD areused for characterization. The repairing layer of large-sized blade is dominated with the coarse columnar grains with severalmillimeters in length, and the grain size is rated as grade 0. After the FH process, the prior austenite grains are significantlyrefined to grade 8. As the hammering temperature increases, the recrystallized austenite grains gradually grow and coarsenowing to the higher ambient temperature. FH at 950 ℃, a temperature slightly higher than the Tre-γ can achieve the austenitegrains with excellent grain refinement effect. Meanwhile, thanks to microstructure inheritance, the room-temperature martensiticis also refined from 4.69 to 2.47 μm, and the typical < 100 > fibre texture content in the deposited layer is obviouslyreduced with the texture intensity reduced from 6.68 to 2.95. Furthermore, the yield strength is increased by about 200 MPa. The main strengthening mechanisms are grain refinement strengthening and dislocation strengthening, and the contributionsto the yield strength are 96.1 MPa and 79 MPa respectively. Additionally, the FH process is also expected to simultaneouslyimprove the formability of the blade repaired layer.
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